Evidence map›Paper›PMID 40150759›Full record

ReviewBioengineering (Basel, Switzerland)2025

Electroencephalographic Biomarkers for Neuropsychiatric Diseases: The State of the Art.

Nayeli Huidobro, Roberto Meza-Andrade, Ignacio Méndez-Balbuena, Carlos Trenado, Maribel Tello Bello, Eduardo Tepichin Rodríguez

Abstract readReview
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Physiologically Guided Modeling for EEG Multichannel Signals.Bioengineering (Basel, Switzerland) · 2026
    Article
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Nayeli HuidobroSchool of Biological Sciences, Universidad Popular Autónoma del Estado de Puebla, Puebla 72000, Mexico.ORCID 0000-0002-2710-6652
Roberto Meza-AndradeDepartamento de Ciencias de la Salud, Universidad de las Américas Puebla, Puebla 72000, Mexico.
Ignacio Méndez-BalbuenaFacultad de Psicología, Benemérita Universidad Autónoma de Puebla, Puebla 72000, Mexico.
Carlos TrenadoInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225 Duesseldorf, Germany.
Maribel Tello BelloEscuela de Ingeniería y Actuaría, Universidad Anáhuac, Puebla 72000, Mexico.ORCID 0000-0003-1233-6874
Eduardo Tepichin RodríguezOptics Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla 72000, Mexico.ORCID 0000-0002-3800-8548

Funding

Vicerrectoría de Investigación y Decanato de Ciencias de la Vida y la Salud de la Universidad Popular Autónoma del Estado de Puebla 10701-1062Vicerrectoría de Investigación y Estudios de Postgrado de la Benemérita Universidad Autónoma de Puebla VIEP-BUAP MEBI-EDH-16
6 · The paper itself

Abstract

Because of their nature, biomarkers for neuropsychiatric diseases were out of the reach of medical diagnostic technology until the past few decades. In recent years, the confluence of greater, affordable computer power with the need for more efficient diagnoses and treatments has increased interest in and the possibility of their discovery. This review will focus on the progress made over the past ten years regarding the search for electroencephalographic biomarkers for neuropsychiatric diseases. This includes algorithms and methods of analysis, machine learning, and quantitative electroencephalography as applied to neurodegenerative and neurodevelopmental diseases as well as traumatic brain injury and COVID-19. Our findings suggest that there is a need for consensus among quantitative electroencephalography researchers on the classification of biomarkers that most suit this field; that there is a slight disconnection between the development of increasingly sophisticated methods of analysis and what they will actually be of use for in the clinical setting; and finally, that diagnostic biomarkers are the most favored type in the field with a few caveats. The main goal of this state-of-the-art review is to provide the reader with a general panorama of the state of the art in this field.

Indexed as

Alzheimer’s diseasebiomarkersCOVID-19depressionLORETAmachine learningmigraineqEEGschizophreniaTBI

Identifiers

PMID40150759
PMCPMC11939446

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.